Because of the iterative nature of Continual Learning scenarios, multiple metrics exist to evaluate model performance. Intuitively, the accuracy of the model after the last task (\(A^t\) ) is important. However, it's not sufficient, e.g. two different models could have the same accuracy at the task \(t\)  but one of them could have better accuracies at task 1, 2, ..., \(t-1\) . Rebuffi et al. [1]} proposed then the average incremental accuracy which is the average of the accuracies after each task: \(\frac{1}{T} \sum _{t=1}^T A^t\) .
